Identifying pathways modulating sleep duration: from genomics to transcriptomics

Abstract

Recognizing that insights into the modulation of sleep duration can emerge by exploring the functional relationships among genes, we used this strategy to explore the genome-wide association results for this trait. We detected two major signalling pathways (ion channels and the ERBB signalling family of tyrosine kinases) that could be replicated across independent GWA studies meta-analyses. To investigate the significance of these pathways for sleep modulation, we performed transcriptome analyses of short sleeping flies' heads (knockdown for the ABCC9 gene homolog; dSur). We found significant alterations in gene-expression in the short sleeping knockdowns versus controls flies, which correspond to pathways associated with sleep duration in our human studies. Most notably, the expression of Rho and EGFR (members of the ERBB signalling pathway) genes was down- and up-regulated, respectively, consistently with the established role of these genes for sleep consolidation in Drosophila. Using a disease multifactorial interaction network, we showed that many of the genes of the pathways indicated to be relevant for sleep duration had functional evidence of their involvement with sleep regulation, circadian rhythms, insulin secretion, gluconeogenesis and lipogenesis.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1

Human gene vs. disease multifactorial interaction network. The network shows interactions between genes from significant pathways (ERBB signaling family of tyrosine kinases and ion channels) identified based on the GWAS datasets (Meta3, green nodes; Meta7, light-blue nodes; overlapping genes between Meta3 and Meta7, orange nodes) and the Drosophila transcriptome GSEA. Only human homologs of the respective Drosophila genes with altered expression in the dSur KD flies in relation to controls (Fig. 2) were included. Relationships between the respective genes and biological processes (diabetes, carbohydrate and lipid metabolism, orange; cardiovascular diseases, beige), protein complexes and phenotypes (abnormal sleep and circadian behaviour, blue) are also shown (decreased activity/expression, grey edges with cross bar; increased activity/expression, green colored arrows; modulated activity/expression, green edges with open diamond). Red edges indicate gene expression for flies pooled every 3 h of the 24 hours period (decreasing expression, cross bar; increased expression in relation to wild-type controls, arrows; ratio RNAi/wt). Protein-protein interactions are displayed as black edges; interlinking genes are shown as grey nodes. A list of interactions with literature references is available in the Supplementary Table S8.

Figure 2

Differentially expressed genes in dSur KD flies vs. controls. Heat map representing a selection of deregulated transcripts homologs to human genes (indicated in brackets) associated with sleep duration in the meta-analysis results used for the GSEA in (a) “Pooled” (Drosophila pooled every 3 h of the 24 hours period) and (b) “Night” (3 h into the night) conditions. A color-coded scale for the normalized expression values is used as follows: yellow and blue represent high and low expression levels in dSur KD with respect to control, respectively. The expression level of each transcript was calculated as the log2 (dSur KD/control), and the complete lists of differentially expressed genes identified by LIMMA software are provided in the Supplementary Table S7.

Figure 3

Signalling by Rho GTPases (upper panel) and Neuronal system pathway (bottom panel). These Reactome pathways show statistically significant enrichment (Fisher Exact test) of DEGs both in “Night” (a, upper panel left, q-value < 0.004; a, bottom panel left, q-value = 0.007) and “Pooled” (b, upper panel right, q-value < 0.001; b, bottom panel right, q-value < 0.001) conditions analysed with Graphite web tool. Coloured nodes represent the differentially expressed genes. The colour of the nodes is proportional to their expression levels represented as log2 (dSur KD/control). Raw p-values were adjusted using Benjamini-Hockberg method (q-value).